Hello all , Today we are here to discuss about the real life opportunities and applications of machine learning in various fields. As Machine Learning is trending technology used nowadays so before learning about this technology why we should learn it . So here we start :-

What is Machine Learning ?

It is a branch of artificial intelligence (AI which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

The scientific discipline of machine learning enables computers to learn without explicit programming. One of the most intriguing technologies one has ever encountered is machine learning (ML).

The rising demand for machine learning engineers is a result of developing technology and the production of enormous volumes of data, or “Big Data.” The typical income for an ML Engineer is 719,646 (IND), or $111,490. (US).

Real life Applications of Machine Learning:-

Traffic Alerts(Maps) –

Now, whenever we need help with directions or traffic, Google Maps is likely the app we utilise. When I recently used the motorway to get to another city, Maps advised me that I was on the fastest route despite the heavy traffic. How does it, however, know that?

Well, it’s a combination of the users of the service right now, historical information about that route gathered over time, and a few tricks obtained from other businesses. Everyone who uses maps provides their location, average speed, and route, which in turn helps Google collect vast amounts of data on traffic and predict incoming traffic so they may modify your route accordingly.

Social Media(Facebook) –

Automatic Friend Tagging Suggestions on Facebook or any other social media site are one of the most popular uses of machine learning. Facebook automatically locates a face that matches its database using face detection and image recognition, then advises that we tag that individual using DeepFace.

DeepFace, a project of Facebook’s Deep Learning division, recognises faces and determines who is in a photo. In addition, it offers Alt Tags (Alternative Tags) for photographs that have previously been posted to Facebook. If we look at the following image on Facebook, for instance, the alt-tag contains a description.

Transportation and Commuting (Uber) –

You have already utilised machine learning to some extent if you have ever used an app to order a cab. It offers a customised application that is exclusive to you. automatically locates you and offers options to travel home, to work, or to any other regular location based on your history and patterns.

For a more precise ETA prediction, a Machine Learning method is applied on top of Historic Trip Data. When machine learning was used, delivery and pickup accuracy increased by 26%.

Products Recommendations –

Consider checking out a product on Amazon without immediately purchasing it. But the following day, as you’re watching YouTube videos, an advertisement for the same thing pops up. When you switch to Facebook, the advertisement is still there. What causes this, then?

This occurs because Google records your search history and makes advertising recommendations based on that data. One of the most innovative uses of machine learning is this. In actuality, product recommendations account for 35% of Amazon’s revenue.

Google Translate –

Recall a time when you visited a foreign country and found it challenging to connect with the natives or locate local landmarks since everything was printed in a different language.

Well, those times are now behind us. In order to offer the best accurate translation of each sentence or set of words, Google’s GNMT (Google Neural Machine Translation) uses Natural Language Processing and thousands of languages and dictionaries. It also makes use of POS Tagging, NER (Named Entity Recognition), and Chunking because the words’ tonality is important. It is among the most effective and popular Machine Learning applications.

Fraud Detection –

Online credit card theft is expected to reach a staggering $32 billion in 2020, according to experts. That is greater than the combined profits of Coca-Cola and JP Morgan Chase. That is a cause for concern. One of the most essential applications of machine learning is fraud detection.

Various payment methods, including credit/debit cards, cellphones, numerous wallets, UPI, and others, have led to an increase in the number of transactions. The number of criminals has increased at the same period, and they are skilled at locating weaknesses.

Every time a customer completes a transaction, the machine learning model carefully examines their profile in search of any unusual patterns. Fraud detection problems are typically posed as classification problems in machine learning.

Opportunities in Machine Learning –

Data Scientist –

A data scientist is a person who crunches numbers and analysis data to find a solution to a specific issue that a company might be experiencing.

The creation of an AI process that can be used as a decision-making tool is a crucial aspect of a data scientist’s job.

In order to address the issues, they must first identify the issues and potential causes of them. Data scientists are needed to analyse the data, understand its consequences, and relate it to the project’s goal.

Computational Linguist –

These days, converting a speech to text is normal practise. Numerous internet programs are available that can do that. The Google Translate programs operate using the same parameter.

Without the assistance of a computational linguist, a machine would not have been able to read, comprehend, convert voice to text, and then back to speech.

A computational linguist must develop rules and use machine learning to replicate human speech in a machine.

Applications such as voice assistants (Siri, Alexa), Translate apps (like Google Translate), data mining, grammar checks, paraphrasing, talk to text and back apps, etc., use computational linguistics.

Human Centered Machine Learning Experts –

Machine Learning is all about teaching machines to identify patterns from data and predict outcomes without being “explicitly programmed.”

In human-centered machine learning, systems combine data-driven solutions with models that emphasise human reasoning to make predictions.

This concept needs to be developed into systems, hence HCML expertise are needed. These devices offer a clever and unique user experience.

Practically, the Human-centered ML model powers all social media feeds. Online shops present additional products under the heading “Based on your search results” that a customer hasn’t actually searched for but may need to view.

Business Intelligence Developer –

A business intelligence developer creates and researches company and market trends and has a solid foundation in apps based on machine learning and data science. They build complex data into models that aid in the expansion of businesses.

A business intelligence developer is in high demand in the current industry because every company is willing to spend a fortune to stay competitive and successful.

Machine Learning Engineer –

Even though it should be the first option on the list, it comes in fifth. This is due to the fact that an engineer who enrols in a machine learning school will inevitably become an ML engineer.

At the heart of all Machine Learning jobs lies data science and research. All Artificial Intelligence projects require Machine Learning engineers.

An algorithm is developed by a machine learning engineer utilizing data to assist a system in developing artificial intelligence.

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